Roy Johnsen is a Professor in the Department of Mechanical and Industrial Engineering at the Norwegian University of Science and Technology (NTNU), specializing in corrosion and surface technology. With a Dr.ing. degree from NTH (1984), he has extensive industry experience from Statoil Research Centre (1985-1991) and CorrOcean (1991-2004), where he expanded the company globally. His current research focuses on hydrogen embrittlement, corrosion protection, and integrity management in offshore systems, with collaborations across Europe, Asia, and the Americas.
Albert M. Berghuis is a Professor in the Department of Biochemistry at McGill University's Faculty of Medicine. His research focuses on structural mechanisms of antibiotic resistance and fungal pathogenesis using advanced techniques including X-ray crystallography, electron microscopy, and computational chemistry. His primary research interests include: Structural basis of antibiotic resistance mechanisms, particularly against aminoglycosides Development of novel antimicrobials through structure-based drug design Structural studies of fungal-specific metabolic pathways for antifungal drug discovery Enzyme mechanisms involving protein-small molecule interactions Analysis of his recent publications reveals a consistent focus on structural elucidation of resistance mechanisms, with particular emphasis on aminoglycoside-modifying enzymes and fungal targets. His work bridges structural biology with therapeutic development, showing strong translational potential in combating antimicrobial resistance. Professor Berghuis maintains the Berghuis Lab with facilities in both the McIntyre Medical Sciences Building and the Francesco Bellini Life Sciences Building. His laboratory employs integrated structural biology approaches to tackle pressing problems in infectious disease treatment.
Kyle W. Klarich is Professor of Medicine and consultant in both the Division of Structural Heart Disease and Division of Echocardiography at Mayo Clinic. His clinical practice and research focus on structural heart disease, cardiac tumors, hypertrophic cardiomyopathies, and valvular heart disease. Dr. Klarich investigates complications prevention and quality-of-life improvement for patients with rare cardiac conditions. As Cardiovascular Disease Fellowship program director since 2010, he is extensively involved in medical education and has received multiple teaching awards including the ACGME's Parker J. Palmer Courage to Teach Award finalist recognition.
Junier Oliva is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill and Lead Faculty of the Master of Applied Data Science program. His research focuses on machine learning, artificial intelligence, and nonparametric statistics, particularly in high-dimensional density estimation, sequential modeling, and learning from complex/structured data. He holds a B.S., M.S., and Ph.D. in Computer Science from Carnegie Mellon University, with prior industry experience at Yahoo! and Uber ATG. Research Interests: Machine learning, artificial intelligence, nonparametric statistics, deep learning, statistical data mining, signal processing, kernel methods, and scalability. His work bridges machine and human learning via collective approaches, emphasizing simple yet flexible models for massive datasets. Awards/Grants: $592K AIM-AHEAD/NIH Grant for Human+AI Collaboration $594K NSF Grant for Scientific Discovery $500K NSF Grant for 'Machine Detectives' Project ACM BCB Best Paper Award (2022) for transparent single-cell classification work Labs/Teams: Director of the LUPA Lab, which develops machine learning techniques for holistic data understanding across domains like healthcare, earth science, and computer vision.
Gauthier Gidel is an Associate Professor at the Department of Computer Science and Operations Research (DIRO) within the Faculty of Arts and Science at Université de Montréal, where he also holds the prestigious Canada CIFAR AI Chair position. He is a core faculty member of Mila, Quebec's AI research institute, and maintains active research collaborations with leading institutions. His academic journey includes a PhD in Computer Science under the supervision of Simon Lacoste-Julien, with internships at Sierra, ElementAI, and DeepMind during his doctoral studies. Dr. Gidel's research spans multiple critical areas in machine learning, with particular emphasis on generative modeling , adversarial machine learning , and variational inequalities for machine learning. His work explores the intersection of optimization theory and practical AI systems, focusing on challenges like LLM safety alignment, multi-agent cooperation, and robustness against adversarial attacks. He is particularly known for his contributions to understanding the theoretical foundations of generative adversarial networks through variational inequality frameworks. His recent publications reveal a strong trend toward addressing critical challenges in large language model safety and alignment, with numerous 2024-2025 papers focusing on adversarial robustness, safety evaluation methodologies, and alignment techniques for LLMs. Simultaneously, his foundational work continues in optimization theory, particularly in variational inequalities and performative prediction, demonstrating his dual focus on practical AI safety concerns and theoretical machine learning foundations. Canada CIFAR AI Chair Core member of Mila Organizer of popular NeurIPS workshops on smooth games Co-founder of the ICLR blog post track Dr. Gidel actively supervises an extensive research group with approximately 10 current graduate students and numerous alumni who have secured positions at leading institutions including Inria Lyon, Oxford, and industry research labs. His research is supported by multiple substantial grants from CRSNG, MITACS, and IVADO, including the prestigious CRSNG Discovery Grant program and MITACS Acceleration Québec projects focused on fraud detection in music streaming and conditional generation. His laboratory maintains strong connections with both academic and industry partners, fostering a collaborative environment focused on advancing AI safety and theoretical understanding.
Ulrich Berger is a Professor of Economics at the Department of Economics of WU Vienna University of Economics and Business (WU Vienna). He serves as Editor-in-Chief of the journal Games and is actively involved in promoting science through the Vienna Skeptics Society. His research focuses on game theory, including non-cooperative, evolutionary, behavioral, and experimental variants. He has been recognized with multiple awards, including the WU Best Paper Award and an Outstanding Reviewer Award. His work explores dynamics of cooperation, reputation systems, and strategic behavior in economic contexts. Key research areas include evolutionary stability in reputation games, indirect reciprocity, and cognitive hierarchies in strategic interactions. His publications span peer-reviewed journals like PLoS ONE and Scientific Reports , as well as popular science articles in outlets like derStandard.at . Berger has led research projects on topics such as cognitive hierarchies in minimizer games and has contributed to policy discussions on access pricing in telecommunications. His academic contributions reflect a blend of theoretical rigor and practical engagement, with recent work emphasizing evolutionary mechanisms of deterrence and experimental validation of equilibrium concepts.
Luca Varani is a Professor and Group Leader of the Structural Biology group at the Institute for Research in Biomedicine (IRB), affiliated with the Università della Svizzera italiana in Bellinzona, Switzerland. His research focuses on understanding the molecular mechanisms of antibody-pathogen interactions and engineering novel therapeutic antibodies. Education: Chemistry degree from University of Milan, PhD from MRC-Laboratory of Molecular Biology (University of Cambridge) Former postdoc at Stanford with EMBO fellowship Founder of CLBiotech (2022), a nanobody discovery and engineering startup Varani's research spans structural biology, immunology, and biophysics with emphasis on viral pathogenesis and antibody engineering. His work combines experimental and computational approaches to study antibody-antigen interactions, particularly against emerging pathogens like SARS-CoV-2, Zika, and Dengue viruses. His group has pioneered structure-guided antibody engineering techniques that have led to multiple high-impact publications in journals like Nature, Cell, and Science. Analysis of Varani's recent publications reveals a strong focus on SARS-CoV-2 antibody responses, with significant contributions to understanding neutralizing mechanisms, viral escape, and therapeutic antibody development. His work also extends to prion diseases, cancer immunology, and flaviviruses, demonstrating a multidisciplinary approach that bridges structural biology with translational medicine. As a reviewer for high-impact journals and international granting agencies, Varani contributes significantly to the scientific community. He also serves as an evaluator for European startup accelerator programs and consults for antibody biotechnology companies, translating academic research into practical applications. Varani leads a highly multidisciplinary research team that employs techniques ranging from NMR spectroscopy and X-ray crystallography to cellular assays and computational modeling. His laboratory has been instrumental in developing bispecific antibodies against SARS-CoV-2 and other pathogens, with several candidates advancing toward clinical trials.
Saud Alhusaini MD PhD is an Assistant Professor of Neurology at the Warren Alpert Medical School of Brown University and serves as a Neurologist/Movement Disorders Specialist at Rhode Island Hospital. His research integrates imaging genomics and multimodal brain imaging approaches to investigate neurological disorders including Parkinson's disease, essential tremor, and epilepsy. He is affiliated with the Carney Institute for Brain Science and collaborates extensively with clinicians, geneticists, electrophysiologists, MRI specialists, neuropsychologists, and data scientists. Education: PhD from the Royal College of Surgeons in Ireland (RCSI) MSc in Neuroscience from Trinity College Dublin MD from University of Dublin, School of Medicine Adult neurology residency at McGill University/Montreal Neurological Institute Clinical research fellowship at Yale School of Medicine Clinical fellowship at Stanford University Medical Center Dr. Alhusaini's research focuses on identifying key endophenotypes and subclinical biomarkers to elucidate the underlying mechanisms of complex neurological conditions. His work spans multiple areas including movement disorders, epilepsy, and brain structure genetics. He has made significant contributions to understanding the genetic architecture of brain structures through his involvement with the ENIGMA consortium, which conducts large-scale collaborative analyses of neuroimaging and genetic data across institutions worldwide. An analysis of his publication record reveals a consistent pattern of high-impact research at the intersection of neurology, genetics, and advanced imaging techniques. His recent work demonstrates particular expertise in Parkinson's disease genetics, epilepsy network analysis, and movement disorder diagnostics. The breadth of his research, spanning from basic genetic mechanisms to clinical applications, highlights his comprehensive approach to understanding neurological disorders. Dr. Alhusaini has received funding from the Rhode Island Research Foundation, Brown Physicians, Inc., and Advance RI-CTR to support his research initiatives. His collaborative approach is evident through his numerous multi-institutional projects and extensive co-author network across Brown University departments including Neurology, Neurosurgery, and Pathology and Laboratory Medicine.
Alex Arenas is a Full Professor in the Department of Computer Engineering and Mathematics at Universitat Rovira i Virgili (URV), Tarragona, Spain. He is also an External Faculty member at the Complexity Science Hub in Vienna and Chief of Complex Systems Science at the Pacific Northwest National Laboratory, USA. His research spans complex systems, network science, computational epidemiology, and multilayer dynamics, with applications in public health, neuroscience, and social systems. Research Interests: His work focuses on the physics of multilayer networked systems, particularly the interplay between structure and function in complex networks. Key areas include synchronization, epidemic modeling, network medicine, the physics of the microbiome, and higher-order interactions in spreading processes. He investigates dynamic transitions using functional multilayer frameworks and develops models for real-world systems like urban mobility and misinformation diffusion. The recent articles highlight a strong trend in computational epidemiology, especially post-COVID modeling of vaccination strategies, rebound dynamics, and wastewater surveillance. There is also significant work on synchronization in oscillator networks, chimera states, and higher-order network effects, reflecting a deep engagement with nonlinear dynamics and theoretical network science. Applications span medicine, urban planning, and social systems. Scientific Awards: Fellow, American Physical Society (2018) Fellow, Network Science Society (2020) ICREA Academia (2011, 2017, 2022) Narcís Monturiol Medal (2022) Web Science Trust Test of Time Award (2024) Complex Systems Society Senior Award (2024) Advising and Grants: Arenas has supervised numerous PhD students and postdoctoral researchers, though specific names are not listed. He has been Principal Investigator on 47 research projects, including EU FP7 projects, a James S. McDonnell Foundation grant, and Horizon Europe's CREXDATA project. He has served as an editor for Physical Review E , Journal of Complex Networks , and Network Neuroscience , and has reviewed for major funding agencies including ERC, MINECO, and international bodies. Labs and Teams: He leads the Alephsys Lab at URV, which develops tools like Radatools for network analysis and community detection. His team focuses on interdisciplinary modeling of real-world complex systems using data-driven and theoretical approaches.
Miler T. Lee is an Associate Professor at the University of Pittsburgh , focusing on gene regulation during early embryonic development through high-throughput experimental and computational genomics. He earned his Ph.D. in Genomics and Computational Biology in 2009 from the University of Pennsylvania under Dr. Junhyong Kim, followed by postdoctoral work with Dr. Antonio Giraldez at Yale University. Joining the university in 2016, his research spans maternal-to-zygotic transition (MZT), RNA stability, pluripotency networks, and evolutionary developmental biology, utilizing model organisms like zebrafish, Xenopus, and Hydractinia symbiolongicarpus. Key Research Themes: Maternally inherited RNA dynamics during embryogenesis Mechanisms of RNA degradation and transcriptome remodeling Evolution of pluripotency networks in hybrid species Role of zinc signaling in fertilization barriers Computational tools for RNA regulation and sensing Scientific Awards: Pan-American Society for Evolutionary Developmental Biology Junior Faculty Award (2024) Outstanding New Investigator – International Xenopus Board (2023) Basil O'Connor Scholar – March of Dimes (2017-2019) Recent publications highlight his work on enhancer classification, RNA degradation mechanisms, and cross-species MZT comparisons. His lab develops innovative methods like RESA for regulatory sequence analysis and studies evolutionary divergence in RNA localization patterns. While the articles span computational and experimental approaches, they consistently address RNA's role in cellular identity, developmental timing, and evolutionary adaptation. Applications include understanding pluripotency, designing RNA biosensors, and elucidating fertilization barriers. Prospective Ph.D. students are encouraged to contact him for opportunities in gene regulation, development, evo-devo, and computational genomics.
Dr. Barbara L. Hempstead is a Professor of Neuroscience and Medicine at Weill Cornell Medical College, where she has held positions since 2001 and 2002 respectively. Her research focuses on neurotrophin signaling mechanisms, particularly the roles of BDNF and its receptors in neuroinflammation, synaptic plasticity, and neurodegenerative diseases. She has made significant contributions to understanding proBDNF/proNGF signaling pathways in neuronal apoptosis and vascular biology. Education: M.D., Ph.D., Washington University School of Medicine (1982) B.A., Tufts University (1976) Dr. Hempstead's work bridges molecular neuroscience and cardiovascular biology, with a particular interest in receptor stoichiometry (p75NTR, TrkB), neurotrophin-induced synaptic remodeling, and therapeutic applications of neurotrophin modulators in Huntington's disease and post-seizure neuronal injury. Her lab investigates how genetic variants like BDNF Val66Met influence anxiety-related behaviors, social memory, and neurodegenerative disease progression through altered neurotrophin trafficking and signaling. Her recent publications highlight neuroinflammatory mechanisms (2023), immune-neurotrophin interactions (2022), and molecular pathways involving BDNF prodomain structure (2020) and SorCS2-mediated receptor trafficking (2017-2020). While no scientific awards are explicitly mentioned in the scraped text, her funded research (National Institute on Aging, NIMH) demonstrates sustained recognition of her work in neurotrophin biology. Dr. Hempstead's lab develops in vitro and in vivo models to study neurotrophin-receptor dynamics, including 3D culture systems for angiogenesis research and transgenic mouse models for Huntington's disease. Her interdisciplinary approach combines molecular neurobiology with vascular physiology to uncover novel therapeutic targets for neurological and cardiovascular conditions.
Dr. Ruth F. Lucas, PhD, RNC, CLS, is an Associate Professor at the University of Connecticut School of Nursing. Her research focuses on breastfeeding equity through biopsychosocial and molecular mechanisms, infant breastfeeding biomechanics, and pain self-management. Education: PhD in Nursing from University of Illinois at Chicago (2011) Dr. Lucas leads projects translating the PROMPT study for WIC-eligible populations and testing biomedical lactation devices for real-time intraoral pressure measurement. Her work integrates genetic factors (e.g., COMT and OXTR variants) with social determinants of health, particularly in African American communities. Her recent research trends span genomics education for nurses, maternity care deserts in the U.S., breastfeeding self-efficacy metrics, and global lactation disparities. Current efforts include competency frameworks for genomics nurse educators and meta-ethnographies on diverse breastfeeding experiences. Contact: ruth.lucas@uconn.edu
Dr. Jonathan Bones is an Associate Professor in the School of Chemical and Bioprocess Engineering at University College Dublin (UCD) and Principal Investigator of the Characterisation and Comparability Group at NIBRT. His research focuses on analytical methods for biopharmaceuticals, including liquid chromatography-mass spectrometry (LC-MS) for protein characterization, glycomics, and process optimization. He holds a BSc and PhD in Analytical Chemistry from Dublin City University. His work has been recognized through inclusion in the Medicine Maker Power List. He leads a team of 18 researchers, supported by SFI, EI, and industry partnerships. Education: BSc in Analytical Science (Chemistry), Dublin City University PhD in Analytical Chemistry, Dublin City University Research Interests: Development of advanced LC-MS platforms for glycomics, proteomics, and bioprocess analysis. Key areas include: Quantitative proteomics/metabolomics for bioprocess monitoring Liquid phase separations for complex bioanalysis Process analytical technology (PAT) His group collaborates with ThermoFisher Scientific on analytical workflows for biopharmaceutical characterization. Articles Trends: Recent work emphasizes analytical methods for AAV vector characterization, biosimilar comparability via MAM/iMAM, and process clearance of excipients. Over 126 publications highlight his contributions to biopharmaceutical quality control and process understanding. Awards: Medicine Maker Power List (2023): Top 100 influential scientists in biopharmaceutical manufacturing and analysis Advising & Grants: Supervises PhD students in bioprocessing and analytical chemistry Funding from Science Foundation Ireland (SFI), Enterprise Ireland (EI), and EU FP7 Industry collaborations with ThermoFisher Scientific and Bristol Myers Squibb Labs & Teams: Leads the Characterisation and Comparability Lab at NIBRT, focused on cutting-edge analytical tools for bioprocess development and product quality assurance.
Professor Dario Alessi is a leading academic at the University of Dundee's School of Life Sciences, serving as the Director of the MRC Protein Phosphorylation Unit (MRC PPU) and Professor of Signal Transduction. He earned his BSc (1988) and PhD (1991) from the University of Birmingham. His research focuses on protein phosphorylation and ubiquitylation pathways, particularly the LRRK2 kinase pathway linked to Parkinson's disease. He has made groundbreaking contributions to understanding LRRK2's role in neurodegeneration, including its interaction with Rab proteins and scaffolding molecules like RILPL1. School of Life Sciences, University of Dundee MRC PPU Director since 2012 Signal Transduction Therapy Unit Director His work combines molecular biology, biochemistry, and collaborative industry partnerships to advance therapeutic strategies for Parkinson's disease. Key research areas include LRRK2 activation mechanisms, Rab protein phosphorylation, and lysosomal dysfunction. Alessi has trained over 30 graduate students and 40 postdocs, many now in academic and industry leadership roles. Notable awards include the EMBO Gold Medal (2005), the Robert A. Pritzker Prize for Leadership in Parkinson’s Research (2023), and an OBE (2023) for contributions to medical science. His lab promotes open science, sharing reagents and protocols globally through platforms like MRC Pure Agents and LRRK2.bio. Current projects include investigating novel mitochondrial and organelle biology in Parkinson’s, developing biomarkers, and advancing LRRK2 inhibitors through clinical trials. Collaborations span the Michael J. Fox Foundation, Aligning Science Across Parkinson’s, and the UK Dementia Research Initiative.
Tengfei Ma is an Assistant Professor in the Department of Biomedical Informatics at Stony Brook University, with affiliations to Computer Science and Applied Mathematics & Statistics. He holds a Ph.D. from The University of Tokyo, M.S. from Peking University, and B.E. from Tsinghua University. Previously, he was a Research Scientist at IBM T.J. Watson Research Center. His research focuses on machine learning, natural language processing (NLP), and biomedical informatics, particularly deep graph learning, scalable graph methods, and healthcare applications. He has contributed to frameworks like EvolveGCN for dynamic graphs and IGB datasets for graph benchmarks. Key awards include ISWC 2021 Best Paper (Research Track) and IBM Outstanding Research Accomplishments (2019, 2022). His work bridges theory and practice, addressing challenges like over-dilution in GNNs and interpretable time series analysis. Collaborations span interdisciplinary areas, such as AI for wound monitoring and code summarization. He teaches BMI530: Software Development for Biomedical Informatics and is open to graduate students from CS, BMI, and AMS departments. Research highlights include: Deep Graph Learning: Scalability (FastGCN, IGB), dynamic graphs (EvolveGCN), and topology-enhanced GNNs. Healthcare: Models for EHR analysis, medication recommendation (GAMENet), and wearable wound monitoring. NLP: Document summarization, code summarization (CP-BCS), and commonsense generation via knowledge graph compression. Recent projects include AI tools like Influencer for promotional content creation and neural-symbolic models for interpretable time series analysis. His lab explores foundational AI for healthcare, code analysis, and graph systems.
